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Dataset of inertial measurements of smartphones and smartwatches for human activity recognition
dc.contributor.author | Matey-Sanz, Miguel | |
dc.contributor.author | Casteleyn, Sven | |
dc.contributor.author | Granell, Carlos | |
dc.date.accessioned | 2024-02-07T11:12:21Z | |
dc.date.available | 2024-02-07T11:12:21Z | |
dc.date.issued | 2023-11-17 | |
dc.identifier.citation | MATEY-SANZ, Miguel; CASTELEYN, Sven; GRANELL, Carlos. Dataset of inertial measurements of smartphones and smartwatches for human activity recognition. Data in Brief, 2023, vol. 51, p. 109809. | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/205731 | |
dc.description.abstract | This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches. | ca_CA |
dc.format.extent | 9 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.relation | ERDF A way of making Europe | ca_CA |
dc.rights | © 2023 The Author(s). Published by Elsevier Inc. | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | HAR | ca_CA |
dc.subject | mobile devices | ca_CA |
dc.subject | inertial sensors | ca_CA |
dc.subject | heterogeneous subjects | ca_CA |
dc.subject | cross-subject evaluation | ca_CA |
dc.title | Dataset of inertial measurements of smartphones and smartwatches for human activity recognition | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1016/j.dib.2023.109809 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Spanish Ministry of Science, Innovation and Universities | ca_CA |
project.funder.name | MCIN/AEI/10.13039/501100011033 | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
oaire.awardNumber | FPU19/05352 | ca_CA |
oaire.awardNumber | PID2020-120250RB-I00 | ca_CA |
oaire.awardNumber | PID2022-1404475OB-C21 | ca_CA |
oaire.awardNumber | PID2022-1404475OB-C22 | ca_CA |
oaire.awardNumber | CIAICO/2022/111 | ca_CA |
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